Abstract
We consider the sensor management problem arising in using a multi-mode sensor to track moving and stopped targets. The sensor management problem is to determine what measurements to take in time so as to optimize the utility of the collected data. Finding the best sequence of measurements is a hard combinatorial problem due to many factors, including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics is due in part to the sensor dwell-time depending on the sensor mode, targets randomly starting and stopping, and the uncertainty in the sensor detection process. For such a sensor management problem, we propose a novel, computationally efficient, farsighted algorithm based on an approximate dynamic programming methodology. The algorithm's complexity is polynomial in the number of targets. We evaluate this algorithm against a myopic algorithm optimizing an information-theoretic scoring criterion. Our simulation results indicate that the farsighted algorithm performs better with respect to the average time the track error is below a specified goal value.
| Original language | English (US) |
|---|---|
| Title of host publication | 2005 7th International Conference on Information Fusion, FUSION |
| Publisher | IEEE Computer Society |
| Pages | 566-573 |
| Number of pages | 8 |
| Volume | 1 |
| ISBN (Print) | 0780392868, 9780780392861 |
| DOIs | |
| State | Published - 2005 |
| Externally published | Yes |
| Event | 2005 8th International Conference on Information Fusion, FUSION - Philadelphia, PA, United States Duration: Jul 25 2005 → Jul 28 2005 |
Other
| Other | 2005 8th International Conference on Information Fusion, FUSION |
|---|---|
| Country | United States |
| City | Philadelphia, PA |
| Period | 7/25/05 → 7/28/05 |
Fingerprint
Keywords
- Farsighted strategy
- Sensor management
- Stochastic dynamic programming
- Tracking
ASJC Scopus subject areas
- Engineering(all)
Cite this
Farsighted sensor management strategies for move/stop tracking. / Nedich, Angelia; Schneider, Michael K.; Washburn, Robert B.
2005 7th International Conference on Information Fusion, FUSION. Vol. 1 IEEE Computer Society, 2005. p. 566-573 1591905.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Farsighted sensor management strategies for move/stop tracking
AU - Nedich, Angelia
AU - Schneider, Michael K.
AU - Washburn, Robert B.
PY - 2005
Y1 - 2005
N2 - We consider the sensor management problem arising in using a multi-mode sensor to track moving and stopped targets. The sensor management problem is to determine what measurements to take in time so as to optimize the utility of the collected data. Finding the best sequence of measurements is a hard combinatorial problem due to many factors, including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics is due in part to the sensor dwell-time depending on the sensor mode, targets randomly starting and stopping, and the uncertainty in the sensor detection process. For such a sensor management problem, we propose a novel, computationally efficient, farsighted algorithm based on an approximate dynamic programming methodology. The algorithm's complexity is polynomial in the number of targets. We evaluate this algorithm against a myopic algorithm optimizing an information-theoretic scoring criterion. Our simulation results indicate that the farsighted algorithm performs better with respect to the average time the track error is below a specified goal value.
AB - We consider the sensor management problem arising in using a multi-mode sensor to track moving and stopped targets. The sensor management problem is to determine what measurements to take in time so as to optimize the utility of the collected data. Finding the best sequence of measurements is a hard combinatorial problem due to many factors, including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics is due in part to the sensor dwell-time depending on the sensor mode, targets randomly starting and stopping, and the uncertainty in the sensor detection process. For such a sensor management problem, we propose a novel, computationally efficient, farsighted algorithm based on an approximate dynamic programming methodology. The algorithm's complexity is polynomial in the number of targets. We evaluate this algorithm against a myopic algorithm optimizing an information-theoretic scoring criterion. Our simulation results indicate that the farsighted algorithm performs better with respect to the average time the track error is below a specified goal value.
KW - Farsighted strategy
KW - Sensor management
KW - Stochastic dynamic programming
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=33847163744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847163744&partnerID=8YFLogxK
U2 - 10.1109/ICIF.2005.1591905
DO - 10.1109/ICIF.2005.1591905
M3 - Conference contribution
AN - SCOPUS:33847163744
SN - 0780392868
SN - 9780780392861
VL - 1
SP - 566
EP - 573
BT - 2005 7th International Conference on Information Fusion, FUSION
PB - IEEE Computer Society
ER -